Fast, Optimal, and Targeted Predictions using Parametrized Decision Analysis

• URL: http://arxiv.org/abs/2006.13107v2
• Date: Sat, 10 Oct 2020 16:39:37 GMT
• Title: Fast, Optimal, and Targeted Predictions using Parametrized Decision Analysis
• Authors: Daniel R. Kowal
• Abstract summary: We develop a class of parametrized actions for Bayesian decision analysis that produce optimal, scalable, and simple targeted predictions. Predictions are constructed for physical activity data from the National Health and Nutrition Examination Survey.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Prediction is critical for decision-making under uncertainty and lends validity to statistical inference. With targeted prediction, the goal is to optimize predictions for specific decision tasks of interest, which we represent via functionals. Although classical decision analysis extracts predictions from a Bayesian model, these predictions are often difficult to interpret and slow to compute. Instead, we design a class of parametrized actions for Bayesian decision analysis that produce optimal, scalable, and simple targeted predictions. For a wide variety of action parametrizations and loss functions--including linear actions with sparsity constraints for targeted variable selection--we derive a convenient representation of the optimal targeted prediction that yields efficient and interpretable solutions. Customized out-of-sample predictive metrics are developed to evaluate and compare among targeted predictors. Through careful use of the posterior predictive distribution, we introduce a procedure that identifies a set of near-optimal, or acceptable targeted predictors, which provide unique insights into the features and level of complexity needed for accurate targeted prediction. Simulations demonstrate excellent prediction, estimation, and variable selection capabilities. Targeted predictions are constructed for physical activity data from the National Health and Nutrition Examination Survey (NHANES) to better predict and understand the characteristics of intraday physical activity.

Related papers

• Prediction-Oriented Bayesian Active Learning [51.426960808684655]
  Expected predictive information gain (EPIG) is an acquisition function that measures information gain in the space of predictions rather than parameters. EPIG leads to stronger predictive performance compared with BALD across a range of datasets and models.
  arXiv  Detail & Related papers  (2023-04-17T10:59:57Z)
• Robust Design and Evaluation of Predictive Algorithms under Unobserved Confounding [2.8498944632323755]
  We propose a unified framework for the robust design and evaluation of predictive algorithms in selectively observed data. We impose general assumptions on how much the outcome may vary on average between unselected and selected units. We develop debiased machine learning estimators for the bounds on a large class of predictive performance estimands.
  arXiv  Detail & Related papers  (2022-12-19T20:41:44Z)
• Finding Islands of Predictability in Action Forecasting [7.215559809521136]
  We show that future action sequences are more accurately modeled with variable, rather than one, levels of abstraction. We propose a combination Bayesian neural network and hierarchical convolutional segmentation model to both accurately predict future actions and optimally select abstraction levels.
  arXiv  Detail & Related papers  (2022-10-13T21:01:16Z)
• Making Decisions under Outcome Performativity [9.962472413291803]
  We introduce a new optimality concept -- performative omniprediction. A performative omnipredictor is a single predictor that simultaneously encodes the optimal decision rule. We show that efficient performative omnipredictors exist, under a natural restriction of performative prediction.
  arXiv  Detail & Related papers  (2022-10-04T17:04:47Z)
• Predicting from Predictions [18.393971232725015]
  We study how causal effects of predictions on outcomes can be identified from observational data. We show that supervised learning that predict from predictions can find transferable functional relationships between features, predictions, and outcomes.
  arXiv  Detail & Related papers  (2022-08-15T16:57:02Z)
• What Should I Know? Using Meta-gradient Descent for Predictive Feature Discovery in a Single Stream of Experience [63.75363908696257]
  computational reinforcement learning seeks to construct an agent's perception of the world through predictions of future sensations. An open challenge in this line of work is determining from the infinitely many predictions that the agent could possibly make which predictions might best support decision-making. We introduce a meta-gradient descent process by which an agent learns what predictions to make, 2) the estimates for its chosen predictions, and 3) how to use those estimates to generate policies that maximize future reward.
  arXiv  Detail & Related papers  (2022-06-13T21:31:06Z)
• Uncertainty estimation of pedestrian future trajectory using Bayesian approximation [137.00426219455116]
  Under dynamic traffic scenarios, planning based on deterministic predictions is not trustworthy. The authors propose to quantify uncertainty during forecasting using approximation which deterministic approaches fail to capture. The effect of dropout weights and long-term prediction on future state uncertainty has been studied.
  arXiv  Detail & Related papers  (2022-05-04T04:23:38Z)
• Efficient and Differentiable Conformal Prediction with General Function Classes [96.74055810115456]
  We propose a generalization of conformal prediction to multiple learnable parameters. We show that it achieves approximate valid population coverage and near-optimal efficiency within class. Experiments show that our algorithm is able to learn valid prediction sets and improve the efficiency significantly.
  arXiv  Detail & Related papers  (2022-02-22T18:37:23Z)
• Dense Uncertainty Estimation [62.23555922631451]
  In this paper, we investigate neural networks and uncertainty estimation techniques to achieve both accurate deterministic prediction and reliable uncertainty estimation. We work on two types of uncertainty estimations solutions, namely ensemble based methods and generative model based methods, and explain their pros and cons while using them in fully/semi/weakly-supervised framework.
  arXiv  Detail & Related papers  (2021-10-13T01:23:48Z)
• Optimized conformal classification using gradient descent approximation [0.2538209532048866]
  Conformal predictors allow predictions to be made with a user-defined confidence level. We consider an approach to train the conformal predictor directly with maximum predictive efficiency. We test the method on several real world data sets and find that the method is promising.
  arXiv  Detail & Related papers  (2021-05-24T13:14:41Z)
• Counterfactual Predictions under Runtime Confounding [74.90756694584839]
  We study the counterfactual prediction task in the setting where all relevant factors are captured in the historical data. We propose a doubly-robust procedure for learning counterfactual prediction models in this setting.
  arXiv  Detail & Related papers  (2020-06-30T15:49:05Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.